blip-caption
autodistill-metaclip
blip-caption | autodistill-metaclip | |
---|---|---|
2 | 1 | |
101 | 16 | |
- | - | |
4.0 | 6.4 | |
8 months ago | 5 months ago | |
Python | Python | |
- | GNU General Public License v3.0 or later |
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blip-caption
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Bash One-Liners for LLMs
I've been gleefully exploring the intersection of LLMs and CLI utilities for a few months now - they are such a great fit for each other! The unix philosophy of piping things together is a perfect fit for how LLMs work.
I've mostly been exploring this with my https://llm.datasette.io/ CLI tool, but I have a few other one-off tools as well: https://github.com/simonw/blip-caption and https://github.com/simonw/ospeak
I'm puzzled that more people aren't loudly exploring this space (LLM+CLI) - it's really fun.
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MetaCLIP – Meta AI Research
I suggest trying BLIP for this. I've had really good results from that.
https://github.com/salesforce/BLIP
I built a tiny Python CLI wrapper for it to make it easier to try: https://github.com/simonw/blip-caption
autodistill-metaclip
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MetaCLIP – Meta AI Research
I have been playing with MetaCLIP this afternoon and made https://github.com/autodistill/autodistill-metaclip as a pip installable version. The Facebook repository has some guidance but you have to pull the weights yourself, save them, etc.
My inference function (model.predict("image.png")) return an sv.Classifications object that you can load into supervision for processing (i.e. get top k) [1].
The paper [2] notes the following in terms of performance:
> In Table 4, we observe that MetaCLIP outperforms OpenAI CLIP on ImageNet and average accuracy across 26 tasks, for 3 model scales. With 400 million training data points on ViT-B/32, MetaCLIP outperforms CLIP by +2.1% on ImageNet and by +1.6% on average. On ViT-B/16, MetaCLIP outperforms CLIP by +2.5% on ImageNet and by +1.5% on average. On ViT-L/14, MetaCLIP outperforms CLIP by +0.7% on ImageNet and by +1.4% on average across the 26 tasks.
[1] https://github.com/autodistill/autodistill-metaclip
What are some alternatives?
MetaCLIP - ICLR2024 Spotlight: curation/training code, metadata, distribution and pre-trained models for MetaCLIP; CVPR 2024: MoDE: CLIP Data Experts via Clustering
clip-interrogator - Image to prompt with BLIP and CLIP